U.S. patent application number 16/692160 was filed with the patent office on 2020-08-20 for operating state evaluation method and operating state evaluation device.
The applicant listed for this patent is MITSUBISHI HEAVY INDUSTRIES, LTD.. Invention is credited to Masayuki HASHIMOTO, Kentaro HAYASHI, Yoshikatsu IKAWA, Takahiro NAKANO, Eriko SHINKAWA, Atsushi YUGE.
Application Number | 20200263669 16/692160 |
Document ID | 20200263669 / US20200263669 |
Family ID | 1000004548774 |
Filed Date | 2020-08-20 |
Patent Application | download [pdf] |
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United States Patent
Application |
20200263669 |
Kind Code |
A1 |
SHINKAWA; Eriko ; et
al. |
August 20, 2020 |
OPERATING STATE EVALUATION METHOD AND OPERATING STATE EVALUATION
DEVICE
Abstract
An operating condition of a wind turbine facility or at least
one wind turbine is acquired, and an estimated value of a
measurable physical quantity corresponding to the operating
condition is calculated. It is determined whether an abnormality is
present in the wind turbine by comparing the estimated value and
the actual value.
Inventors: |
SHINKAWA; Eriko; (Tokyo,
JP) ; HAYASHI; Kentaro; (Tokyo, JP) ; YUGE;
Atsushi; (Tokyo, JP) ; HASHIMOTO; Masayuki;
(Tokyo, JP) ; IKAWA; Yoshikatsu; (Tokyo, JP)
; NAKANO; Takahiro; (Tokyo, JP) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
MITSUBISHI HEAVY INDUSTRIES, LTD. |
Tokyo |
|
JP |
|
|
Family ID: |
1000004548774 |
Appl. No.: |
16/692160 |
Filed: |
November 22, 2019 |
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
F03D 17/00 20160501;
G01M 99/005 20130101 |
International
Class: |
F03D 17/00 20060101
F03D017/00; G01M 99/00 20060101 G01M099/00 |
Foreign Application Data
Date |
Code |
Application Number |
Feb 14, 2019 |
JP |
2019-024353 |
May 9, 2019 |
JP |
2019-089031 |
Claims
1. A method for evaluating an operating state of a wind turbine
facility including at least one wind turbine, comprising: a step of
acquiring an operating condition of the wind turbine facility or
the at least one wind turbine; a step of calculating an estimated
value of a physical quantity measurable on the at least one wind
turbine and corresponding to the operating condition; a step of
acquiring an actual value corresponding to the physical quantity;
and a step of determining whether an abnormality is present in the
at least one wind turbine by comparing the estimated value and the
actual value.
2. The method according to claim 1, wherein the estimated value is
calculated by inputting the operating condition as an input
parameter to a physical model of the wind turbine facility or the
at least one wind turbine.
3. The method according to claim 1, wherein the estimated value is
calculated by inputting the operating condition as an input
parameter to a machine learning model of the wind turbine facility
or the at least one wind turbine.
4. The method according to claim 1, wherein the at least one wind
turbine includes a plurality of wind turbines, and wherein the
operating condition is obtained by averaging parameters acquired
from each of the plurality of wind turbines.
5. The method according to claim 1, wherein the at least one wind
turbine includes a plurality of wind turbines, and wherein the
estimated value is obtained by applying statistical processing to
the actual value acquired from each of the plurality of wind
turbines.
6. The method according to claim 5, wherein the estimated value is
an average of the actual value acquired from each of the plurality
of wind turbines.
7. The method according to claim 1, wherein a difference between
the estimated value and the actual value is calculated, and it is
determined whether an abnormality is present based on whether the
difference exceeds a threshold.
8. The method according to claim 1, wherein the at least one wind
turbine includes a plurality of wind turbines, and wherein the
method includes a step of identifying a wind turbine having an
abnormality by comparison in behavior of the actual value with
respect to the operating condition among the plurality of wind
turbines.
9. The method according to claim 8, wherein a correlation
coefficient between the estimated value and the actual value is
obtained for each of the plurality of wind turbines, and a wind
turbine whose correlation coefficient exceeds a threshold is
determined to have an abnormality.
10. The method according to claim 1, wherein the at least one wind
turbine includes a plurality of wind turbines, wherein the method
further includes: a step of calculating an abnormality degree of
each of the plurality of wind turbines, based on the operating
condition of each of the wind turbines; a step of determining
whether an abnormality is present in each of the plurality of wind
turbines, based on the abnormality degree of each of the wind
turbines, and a step of, if at least one of the plurality of wind
turbines is determined to have an abnormality, verifying an
abnormality positive determination that the at least one of the
plurality of wind turbines has the abnormality, and wherein the
step of verifying the abnormality positive determination includes:
a step of acquiring a determination result regarding one or more
other of the plurality of wind turbines based on the abnormality
degree, in a predetermined period including a timing of acquiring
the operating condition used for calculating the abnormality degree
based on which the abnormality positive determination is made, and
a step of making a first validity determination whether the
abnormality positive determination is valid, based on the number of
wind turbines that are determined to be abnormal based on the
abnormality degree among the one or more other of the plurality of
wind turbines.
11. The method according to claim 10, wherein the step of making
the first validity determination includes determining that the
abnormality positive determination is invalid if the number is less
than a first verification threshold, and determining that the
abnormality positive determination is valid if the number is not
less than the first verification threshold.
12. The method according to claim 10, further comprising a step of
notifying that the abnormality is detected if the abnormality
positive determination is determined to be valid.
13. The method according to claim 1, wherein the at least one wind
turbine includes a plurality of wind turbines, wherein the method
further includes: a step of calculating an abnormality degree of
each of the plurality of wind turbines, based on the operating
condition of each of the wind turbines; a step of determining
whether an abnormality is present in each of the plurality of wind
turbines, based on the abnormality degree of each of the wind
turbines, and a step of, if at least one of the plurality of wind
turbines is determined not to have an abnormality, verifying an
abnormality negative determination that the at least one of the
plurality of wind turbines does not have an abnormality, and
wherein the step of verifying the abnormality negative
determination includes: a step of calculating a statistic of the
abnormality degree of each of the plurality of wind turbines; a
step of calculating a relationship between the abnormality degree
of each of the plurality of wind turbines and the statistic; and a
step of a making a second validity determination whether the
abnormality negative determination is valid for each of the wind
turbines, based on the relationship.
14. The method according to claim 13, further comprising a step of
issuing notification if the abnormality negative determination is
determined to be invalid.
15. The method according to claim 13, wherein the statistic is an
average of the abnormality degree of the plurality of wind
turbines.
16. The method according to claim 13, wherein the relationship is a
deviation between the abnormality degree of each wind turbine and
the statistic.
17. The method according to claim 13, wherein the step of making
the second validity determination includes determining that each
wind turbine is abnormal if the relationship is not less than a
second verification threshold, and determining that each wind
turbine is normal if the relationship is less than the second
verification threshold.
18. The method according to claim 1, wherein the at least one wind
turbine includes a plurality of wind turbines, wherein the method
further includes: a step of calculating an abnormality degree of
each of the plurality of wind turbines, based on the operating
condition of each of the wind turbines; a step of, if at least one
of the plurality of wind turbines is determined to have an
abnormality based on the abnormality degree, verifying the
determination based on the abnormality degree of the other of the
plurality of wind turbines at a timing of acquiring the operating
condition, and a step of, if at least one of the plurality of wind
turbines is determined not to have an abnormality based on the
abnormality degree, verifying the determination based on a strength
of relevance between a statistic calculated from the abnormality
degree of each of the plurality of wind turbines at a timing of
acquiring the operating condition and the abnormality degree of the
at least one of the plurality of wind turbines that is determined
not to have an abnormality.
19. A device for evaluating an operating state of a wind turbine
facility including at least one wind turbine, comprising: an
operating condition acquisition part configured to acquire an
operating condition of the wind turbine facility or the at least
one wind turbine; an estimated value calculation part configured to
calculate an estimated value of a physical quantity measurable on
the at least one wind turbine and corresponding to the operating
condition; an actual value acquisition part configured to acquire
an actual value corresponding to the physical quantity; and a
determination part configured to determine whether an abnormality
is present in the at least one wind turbine by comparison between
the estimated value and the actual value.
20. The device according to claim 19, wherein the at least one wind
turbine includes a plurality of wind turbines, wherein the device
further includes: a calculation part configured to calculate an
abnormality degree of each of the plurality of wind turbines, based
on the operating condition of each of the wind turbines; and a
verification part configured to, if at least one of the plurality
of wind turbines is determined to have an abnormality based on the
abnormality degree, verify the determination based on the
abnormality degree of the other of the plurality of wind turbines
at a timing of acquiring the operating condition, and wherein the
verification part is further configured to, if at least one of the
plurality of wind turbines is determined not to have an abnormality
based on the abnormality degree, verify the determination based on
a strength of relevance between a statistic calculated from the
abnormality degree of each of the plurality of wind turbines at a
timing of acquiring the operating condition and the abnormality
degree of the at least one of the plurality of wind turbines that
is determined not to have an abnormality.
Description
BACKGROUND OF THE INVENTION
1. Technical Field
[0001] This disclosure relates to a method and a device for
evaluating an operating state of a wind turbine facility.
2. Description of the Related Art
[0002] The operating state of a facility can be monitored by
acquiring an actual value of a parameter to be monitored from the
operating facility and comparing the actual value with a
predetermined criterion threshold. In this case, if the acquired
actual value exceeds the threshold, the operating state is judged
to be abnormal.
[0003] JP2010-159710A provides an example of monitoring the
operating state of a facility. In JP2010-159710A, the monitoring
target is a main shaft bearing which supports a main shaft provided
with blades in a wind power generating apparatus. A load acting on
the main shaft bearing is detected, and the state of the main shaft
bearing is evaluated based on the magnitude of the load and is used
for predicting the timing of maintenance, for instance.
SUMMARY OF THE INVENTION
[0004] The threshold used for monitoring the operating state is set
with some margin, assuming various operating states that may occur
in the facility. For instance, in a case where a bearing is
monitored as in Patent Document 1, since the temperature of cooling
oil supplied to the bearing changes according to the on/off state
of a cooler disposed in a supply channel of the cooling oil, it is
difficult to detect an increase in temperature depending on the
operating state of the cooler even when the temperature is assumed
to rise due to an abnormality in the bearing. Taking into
consideration such characteristics, a threshold has to be set with
a large margin in order to accurately detect the abnormality. On
the other hand, if the threshold has a large margin, it is
difficult to detect an abnormality at the moment when the
temperature of the bearing starts to rise, and it is difficult to
determine the abnormality at an early stage.
[0005] Further, the wind power generating apparatus as disclosed in
Patent Document 1 is installed outside and thus is easily affected
by an external environment. In particular, since the ambient
temperature changes due to seasonal fluctuations, the threshold
used for monitoring the operating state of the wind power
generating apparatus has to take a large margin in consideration of
the influence of such an external environment. Therefore, it is
difficult to detect the abnormality at an early stage.
[0006] At least one embodiment of the present invention was made in
view of the above circumstances, and an object thereof is to
provide an operating state evaluation method and an operating state
evaluation device that can accurately and early detect an
abnormality by criteria in accordance with the operating state of a
facility.
[0007] (1) To solve the above problem, an operating state
evaluation method according to at least one embodiment of the
present invention for evaluating an operating state of a wind
turbine facility including at least one wind turbine comprises: a
step of acquiring an operating condition of the wind turbine
facility or the at least one wind turbine; a step of calculating an
estimated value of a physical quantity measurable on the at least
one wind turbine and corresponding to the operating condition; a
step of acquiring an actual value corresponding to the physical
quantity; and a step of determining whether an abnormality is
present in the at least one wind turbine by comparing the estimated
value and the actual value.
[0008] With the above method (1), by comparing the estimated value
calculated according to the operating condition with the actual
value, it is possible to determine the presence of abnormality
based on a criterion corresponding to the operating condition.
Therefore, compared with determination using a criterion set
uniformly regardless of the operating condition, a detailed
abnormality determination can be performed, and the operating state
can be accurately and early evaluated.
[0009] (2) In some embodiments, in the above method (1), the
estimated value is calculated by inputting the operating condition
as an input parameter to a physical model of the wind turbine
facility or the at least one wind turbine.
[0010] With the above method (2), the estimated value corresponding
to the operating condition can be calculated using a physical
model.
[0011] (3) In some embodiments, in the above method (1), the
estimated value is calculated by inputting the operating condition
as an input parameter to a machine learning model of the wind
turbine facility or the at least one wind turbine.
[0012] With the above method (3), the estimated value corresponding
to the operating condition can be calculated using a machine
learning model.
[0013] (4) In some embodiments, in any one of the above methods (1)
to (3), the at least one wind turbine includes a plurality of wind
turbines, and the operating condition is obtained by averaging
parameters acquired from each of the plurality of wind
turbines.
[0014] With the above method (4), by averaging parameters acquired
from each of the plurality of wind turbines and using the average
as the operating condition, it is possible to reduce the influence
of a random disturbance factor that may be input to a specific wind
turbine, and it is possible to achieve more reliable
evaluation.
[0015] (5) In some embodiments, in the above method (1), the at
least one wind turbine includes a plurality of wind turbines, and
the estimated value is obtained by applying statistical processing
to the actual value acquired from each of the plurality of wind
turbines.
[0016] With the above method (5), the estimated value corresponding
to the operating condition can be calculated by applying
statistical processing to the actual value acquired from each of
the plurality of wind turbines.
[0017] (6) In some embodiments, in the above method (5), the
estimated value is an average of the actual value acquired from
each of the plurality of wind turbines.
[0018] With the above method (6), by using the average of the
actual values of the plurality of wind turbines as the estimated
value corresponding to the operating condition, it is possible to
achieve simple and reliable evaluation.
[0019] (7) In some embodiments, in any one of the above methods (1)
to (6), a difference between the estimated value and the actual
value is calculated, and it is determined whether an abnormality is
present based on whether the difference exceeds a threshold.
[0020] With the above method (7), by using the difference between
the estimated value and the actual value as the evaluation
parameter, it is possible to quantitatively evaluate deviation of
the actual value due to an abnormality, and it is possible to
accurately identify a wind turbine having an abnormality.
[0021] (8) In some embodiments, in any one of the above methods (1)
to (7), the at least one wind turbine includes a plurality of wind
turbines, and the method includes a step of identifying a wind
turbine having an abnormality by comparison in behavior of the
actual value with respect to the operating condition among the
plurality of wind turbines.
[0022] With the above method (8), since a wind turbine having an
abnormality exhibits different behavior of the actual value against
the estimated value, by comparing behaviors of the actual value of
each wind turbine against the estimated value, it is possible to
accurately identify a wind turbine having an abnormality.
[0023] (9) In some embodiments, in the above method (8), a
correlation coefficient between the estimated value and the actual
value is obtained for each of the plurality of wind turbines, and a
wind turbine whose correlation coefficient exceeds a threshold is
determined to have an abnormality.
[0024] With the above method (9), by using the correlation
coefficient between the estimated value and the actual value as the
evaluation parameter, it is possible to quantitatively evaluate
deviation of the actual value due to an abnormality, and it is
possible to accurately identify a wind turbine having an
abnormality.
[0025] (10) In some embodiments, in any one of the above methods
(1) to (9), the at least one wind turbine includes a plurality of
wind turbines, and the method further includes: a step of
calculating an abnormality degree of each of the plurality of wind
turbines, based on the operating condition of each of the wind
turbines; a step of determining whether an abnormality is present
in each of the plurality of wind turbines, based on the abnormality
degree of each of the wind turbines, and a step of, if at least one
of the plurality of wind turbines is determined to have an
abnormality, verifying an abnormality positive determination that
the at least one of the plurality of wind turbines has the
abnormality. The step of verifying the abnormality positive
determination includes: a step of acquiring a determination result
regarding one or more other of the plurality of wind turbines based
on the abnormality degree, in a predetermined period including a
timing of acquiring the operating condition used for calculating
the abnormality degree based on which the abnormality positive
determination is made, and a step of making a first validity
determination whether the abnormality positive determination is
valid, based on the number of wind turbines that are determined to
be abnormal based on the abnormality degree among the one or more
other of the plurality of wind turbines.
[0026] For instance, there is a technique of detecting an
abnormality of each of a plurality of wind turbines by calculating
an abnormality degree based on multiple sensor values (operating
condition) detected from multiple sensors disposed on each wind
turbine and comparing the abnormality degree with a threshold
(abnormality determination threshold). In such a technique, if
detection sensitivity is increased by, for instance, setting the
threshold low in order to detect an abnormality at an early stage
before the wind turbine fails, although the occurrence of failure
of the wind turbine can be more reliably prevented, false detection
may occur, such as false abnormality detection when the abnormality
degree temporarily exceeds the threshold due to an external
environmental factor. If the wind turbine in which an abnormality
is detected is stopped for inspection, the operating rate of the
wind turbine decreases as the number of false detections
increases.
[0027] With the above method (10), if at least one of the plurality
of wind turbines is determined to have an abnormality based on the
abnormality degree calculated based on the operating condition
(multiple parameter values), the validity (accuracy) of that
abnormality positive determination is verified based on the number
of abnormality positive determinations in the determination result
regarding the other wind turbines based on the abnormality degree
at the same timing. By ignoring the abnormality positive
determination that is determined to be false on the verification,
it is possible to early detect a sign of an abnormality occurring
in each wind turbine with an increased detection sensitivity while
avoiding false detection based on the abnormality degree of each
wind turbine. Accordingly, it is possible to prevent a reduction in
operating rate due to false detection and an increase in cost.
[0028] (11) In some embodiments, in the above method (10), the step
of making the first validity determination includes determining
that the abnormality positive determination is invalid if the
number is less than a first verification threshold, and determining
that the abnormality positive determination is valid if the number
is not less than the first verification threshold.
[0029] With the above method (11), it is possible to appropriately
judge the validity of the abnormality positive determination.
[0030] (12) In some embodiments, the above method (10) or (11)
further comprises a step of notifying that the abnormality is
detected if the abnormality positive determination is determined to
be valid.
[0031] With the above method (12), if the abnormality positive
determination of each wind turbine is determined to be invalid
(false detection), the abnormality positive determination is not
adopted. Conversely, if the abnormality positive determination is
determined to be valid, a monitor is notified, for instance. Thus,
it is possible to avoid the notification of false detection and the
need for response to this notification such as inspection.
[0032] (13) In some embodiments, in any one of the above methods
(1) to (12), the at least one wind turbine includes a plurality of
wind turbines, and the method further includes: a step of
calculating an abnormality degree of each of the plurality of wind
turbines, based on the operating condition of each of the wind
turbines; a step of determining whether an abnormality is present
in each of the plurality of wind turbines, based on the abnormality
degree of each of the wind turbines, and a step of, if at least one
of the plurality of wind turbines is determined not to have an
abnormality, verifying an abnormality negative determination that
the at least one of the plurality of wind turbines does not have an
abnormality. The step of verifying the abnormality negative
determination includes: a step of calculating a statistic of the
abnormality degree of each of the plurality of wind turbines; a
step of calculating a relationship between the abnormality degree
of each of the plurality of wind turbines and the statistic; and a
step of a making a second validity determination whether the
abnormality negative determination is valid for each of the wind
turbines, based on the relationship.
[0033] For instance, as described above, if detection sensitivity
is decreased by, for instance, increasing the threshold to detect
an abnormality of each of the plurality of wind turbines based on
comparison between the abnormality degree and the threshold
(abnormality determination threshold), although false detection can
be reduced, it is difficult to early detect an abnormality (sign of
abnormality) before each wind turbine fails. For instance, even if
the abnormality degree gradually increases due to an abnormality
occurring in the wind turbine, if the value of the abnormality
degree is not more than the threshold, an abnormality cannot be
detected, and a sign of abnormality cannot be accurately obtained.
Further, as abnormality detection is delayed, a risk of failure of
the wind turbine increases, and the operating rate of the wind
turbine may decrease due to the failure.
[0034] With the above method (13), if at least one of the plurality
of wind turbines is determined not to have an abnormality based on
the abnormality degree calculated based on the operating condition
(multiple parameter values), the validity (accuracy) of that
abnormality negative determination is verified based on the
statistic calculated from the plurality of abnormality degrees at
the same timing. Thus, even if the abnormality degree is not more
than the threshold, it is possible to early detect an abnormality,
and it is possible to prevent a reduction in operating rate due to
failure of the wind turbine and an increase in cost.
[0035] (14) In some embodiments, the above method (13) further
comprises a step of issuing notification if the abnormality
negative determination is determined to be invalid.
[0036] With the above method (14), if the abnormality negative
determination of each wind turbine is determined to be invalid, for
instance, a monitor is notified of abnormality positive
determination. Thus, it is possible to more appropriately detect an
abnormality.
[0037] (15) In some embodiments, in the above method (13) or (14),
the statistic is an average of the abnormality degree of the
plurality of wind turbines.
[0038] With the above method (15), it is possible to appropriately
judge the validity of the abnormality negative determination.
[0039] (16) In some embodiments, in any one of the above methods
(13) to (15), the relationship is a deviation between the
abnormality degree of each wind turbine and the statistic.
[0040] With the above method (16), it is possible to appropriately
judge the validity of the abnormality negative determination, based
on the deviation between the abnormality degree and the statistic
(e.g., average).
[0041] (17) In some embodiments, in any one of the above methods
(13) to (16), the step of making the second validity determination
includes determining that each wind turbine is abnormal if the
relationship is not less than a second verification threshold, and
determining that each wind turbine is normal if the relationship is
less than the second verification threshold.
[0042] With the above method (17), it is possible to appropriately
judge the validity of the abnormality negative determination.
[0043] (18) In some embodiments, in any one of the above methods
(13) to (17), the step of verifying the abnormality negative
determination includes verifying the abnormality negative
determination if none of the plurality of wind turbines is
determined to have an abnormality.
[0044] With the above method (18), the abnormality negative
determination is verified if none of the plurality of wind turbines
is determined to have an abnormality. Thus, the same effect is
achieved as in the above (13) to (17).
[0045] (19) In some embodiments, in any one of the above methods
(1) to (9), the at least one wind turbine includes a plurality of
wind turbines, and the method further includes: a step of
calculating an abnormality degree of each of the plurality of wind
turbines, based on the operating condition of each of the wind
turbines; a step of, if at least one of the plurality of wind
turbines is determined to have an abnormality based on the
abnormality degree, verifying the determination based on the
abnormality degree of the other of the plurality of wind turbines
at a timing of acquiring the operating condition, and a step of, if
at least one of the plurality of wind turbines is determined not to
have an abnormality based on the abnormality degree, verifying the
determination based on a strength of relevance between a statistic
calculated from the abnormality degree of each of the plurality of
wind turbines at a timing of acquiring the operating condition and
the abnormality degree of the at least one of the plurality of wind
turbines that is determined not to have an abnormality.
[0046] With the above method (19), the determination result
regarding the presence or absence of abnormality in each wind
turbine based on the abnormality degree is verified for both cases
where the wind turbine is determined to have an abnormality and not
to have an abnormality. Thus, the same effect is achieved as in the
above (10) and (13).
[0047] (20) To solve the above problem, an operating state
evaluation device according to at least one embodiment of the
present invention for evaluating an operating state of a wind
turbine facility including at least one wind turbine comprises: an
operating condition acquisition part configured to acquire an
operating condition of the wind turbine facility or the at least
one wind turbine; an estimated value calculation part configured to
calculate an estimated value of a physical quantity measurable on
the at least one wind turbine and corresponding to the operating
condition; an actual value acquisition part configured to acquire
an actual value corresponding to the physical quantity; and a
determination part configured to determine whether an abnormality
is present in the at least one wind turbine by comparison between
the estimated value and the actual value.
[0048] With the above configuration (20), by comparing the
estimated value calculated according to the operating condition
with the actual value, it is possible to determine the presence of
abnormality based on a criterion corresponding to the operating
condition. Therefore, compared with determination using a criterion
set uniformly regardless of the operating condition, a detailed
abnormality determination can be performed, and the operating state
can be accurately and early evaluated.
[0049] (21) In some embodiments, in the above configuration (20),
the at least one wind turbine includes a plurality of wind
turbines, and the device further includes: a calculation part
configured to calculate an abnormality degree of each of the
plurality of wind turbines, based on the operating condition of
each of the wind turbines; and a verification part configured to,
if at least one of the plurality of wind turbines is determined to
have an abnormality based on the abnormality degree, verify the
determination based on the abnormality degree of the other of the
plurality of wind turbines at a timing of acquiring the operating
condition and further configured to, if at least one of the
plurality of wind turbines is determined not to have an abnormality
based on the abnormality degree, verify the determination based on
a strength of relevance between a statistic calculated from the
abnormality degree of each of the plurality of wind turbines at a
timing of acquiring the operating condition and the abnormality
degree of the at least one of the plurality of wind turbines that
is determined not to have an abnormality.
[0050] With the above configuration (21), the same effect is
achieved as in the above (19).
[0051] At least one embodiment of the present invention provides an
operating state evaluation method and an operating state evaluation
device that can accurately and early detect an abnormality by
criteria in accordance with the operating state of the
facility.
BRIEF DESCRIPTION OF THE DRAWINGS
[0052] FIG. 1 is an overall configuration diagram of a wind turbine
facility.
[0053] FIGS. 2A and 2B are schematic diagrams of a wind turbine of
FIG. 1. In particular,
[0054] FIG. 2A is a side view of the wind turbine 2, and FIG. 2B is
a front view of the wind turbine 2.
[0055] FIG. 3 is a block diagram showing an interior configuration
and a surrounding configuration of a control unit.
[0056] FIG. 4 is a flowchart showing steps of an operating state
evaluation method according to at least one embodiment of the
present invention.
[0057] FIG. 5 is a schematic diagram of a physical model used in an
estimated value calculation part.
[0058] FIG. 6 is verification result showing estimated values
calculated based on an operating condition and actual values
regarding the temperature of a bearing, plotted against the output
power of a wind turbine.
[0059] FIG. 7 is verification result in which estimated values
calculated by an estimated value calculation part and actual values
acquired by an actual value acquisition part regarding the
temperature of a bearing are plotted for each operating
condition.
[0060] FIG. 8 is a block diagram showing an interior configuration
and a surrounding configuration of a control unit that includes a
first verification part and a second verification part.
[0061] FIG. 9 is a flowchart showing an operating state evaluation
method according to another embodiment of the present
invention.
[0062] FIG. 10 is a diagram for describing an example of
determination by a first verification step according to an
embodiment of the present invention.
[0063] FIG. 11 is a diagram for describing an example of
determination by a second verification step according to an
embodiment of the present invention.
DETAILED DESCRIPTION
[0064] Embodiments of the present invention will now be described
with reference to the accompanying drawings. It is intended,
however, that unless particularly specified, dimensions, materials,
shapes, relative positions and the like of components described in
the embodiments shall be interpreted as illustrative only and not
intended to limit the scope of the present invention.
[0065] FIG. 1 is an overall configuration diagram of a wind turbine
facility 1. FIGS. 2A and 2B are schematic diagrams of a wind
turbine 2 of FIG. 1. FIG. 2A is a side view of the wind turbine 2,
and FIG. 2B is a front view of the wind turbine 2.
[0066] The wind turbine facility 1 includes at least one wind
turbine 2. As shown in
[0067] FIGS. 2A and 2B, the wind turbine 2 includes a rotor 2r
having at least one wind turbine blade 2b and a hub 2h with the
wind turbine blade 2b mounted thereto, a nacelle 10, and a tower 12
supporting the nacelle 10. In this example, the wind turbine 2 has
three wind turbine blades 2b mounted to the hub 2h, and is
configured such that as wind acts on the wind turbine blades 2b,
the rotor 2r including the wind turbine blades 2b and the hub 2h
rotates about the rotational axis of the rotor 2r.
[0068] The wind turbine 2 may be a wind power generating apparatus.
In this case, the nacelle 10 may accommodate a generator and a
power transmission mechanism for transmitting rotation of the rotor
2r to the generator and may be configured such that rotational
energy transmitted to the generator via the power transmission
mechanism from the rotor 2r is converted into electric energy by
the generator.
[0069] The wind turbine facility 1 forms a so-called wind farm
composed of at least one wind turbine 2 disposed in a predetermined
area. In the example of FIG. 1, the wind turbine facility 1
includes a plurality of wind turbines 2 disposed in a predetermined
area. The plurality of wind turbines 2 has the same specification
(same type) and is managed by a control unit 14. The control unit
14 includes an electronic arithmetic device such as a computer and
is connected to each of the wind turbines 2 via a communication
line 15 for transmitting and receiving various data to control the
operating state of the wind turbine facility 1.
[0070] One of the primary functions of the control unit 14 is to
acquire a parameter related to the operating state of the wind
turbine facility 1 and the at least one wind turbine 2 and monitor
the operating state. The parameter related to the operating state
includes various physical quantities indicating the operating state
of the wind turbine facility 1 or the wind turbine 2. For instance,
the parameter may be a physical parameter defining the operating
state (e.g., output power of the wind turbine 2, ambient
temperature, temperature in a specific location, pressure (oil
pressure), current) or may be an electrical signal or an
instruction (e.g., operating information) transmitted to and/or
from the wind turbine facility 1 or the wind turbine 2 for control.
Further, the parameter may be acquired from the entire wind turbine
facility 1, or may be acquired from each wind turbine 2
constituting the wind turbine facility 1, or may be acquired from a
part of the wind turbines 2.
[0071] Evaluation of the operating state of the wind turbine
facility 1 having the above configuration will be described. In the
following, a case where an operating state evaluation method
according to at least one embodiment of the present invention is
performed by an operating state evaluation device configured by
installing a predetermined program on the control unit 14 will be
described. Such an operating state evaluation device may be
configured by installing a program for executing an operating state
evaluation method described later on the control unit 14.
[0072] The program may be installed on the electronic arithmetic
device by reading a storage medium previously storing the program
by a predetermined reader. The storage medium storing the program
and the program itself are also included in the present
invention.
[0073] FIG. 3 is a block diagram showing an interior configuration
and a surrounding configuration of the control unit 14. FIG. 4 is a
flowchart showing steps of the operating state evaluation method
according to at least one embodiment of the present invention.
[0074] As shown in FIG. 3, the control unit 14 includes an
operating condition acquisition part 16, an estimated value
calculation part 18, an actual value acquisition part 20, a
determination part 22, and an output part 24. In FIG. 3, among the
interior configuration of the control unit 14, components related
to the operating state evaluation method according to at least one
embodiment of the present invention are representatively shown as
functional blocks. The control unit 14 may have other functional
blocks. The functional blocks shown in FIG. 3 may be integrated or
further divided.
[0075] When the wind turbine facility 1 is operating, the operating
condition acquisition part 16 acquires an operating condition of
the wind turbine facility 1 or the at least one wind turbine 2
(step S1). That is, the operating condition acquisition target may
be the wind turbine facility 1 or may be the wind turbine 2
constituting the wind turbine facility 1. In the latter case, if
the wind turbine facility 1 includes a plurality of wind turbines
2, the operating condition acquisition target may be all of the
wind turbines 2 or may be a part of the wind turbines 2.
[0076] The operating state acquired by the operating condition
acquisition part 16 includes any parameter related to the operating
state of the wind turbine facility 1 or the at least one wind
turbine 2. That is, as described above, parameters that can be
acquired by the control unit 14 from the wind turbine facility 1 or
the at least one wind turbine 2 are used as the operating
condition. The parameters constituting the operating condition may
be acquired by various sensors disposed on the wind turbine
facility 1 or the wind turbine 2, or may be various electrical
signals transmitted between the wind turbine facility 1, the wind
turbine 2, and the control unit 14.
[0077] In the present embodiment, a case where the operating state
of each wind turbine 2 is evaluated based on the temperature of a
bearing 32 (see FIG. 5) will be described. Accordingly, the
parameters constituting the operating condition include, for
instance, the output power of the wind turbine 2, the ambient
temperature, the temperature in a specific location, and operating
information as parameters necessary for calculating an estimated
value of the temperature of the bearing 32.
[0078] When the operating condition acquisition target is the wind
turbine 2, the operating condition acquisition part 16 may acquire
the operating condition from a single wind turbine 2, or may
acquire the operating conditions from a plurality of wind turbines
2. Since the same type of wind turbines 2 are disposed in a
predetermined area in the wind turbine facility 1, the operating
conditions acquired from the respective wind turbines 2 are likely
to have the same or similar values. Accordingly, by acquiring the
operating conditions from the plurality of wind turbines 2, even if
an improper operating condition is acquired from a specific wind
turbine, the reliability of the operating condition acquired from
each wind turbine 2 can be evaluated by comparison with a proper
operating condition acquired from the other wind turbines 2. In
this case, only operating conditions having enough reliability may
be selected, or the operating conditions may be statistically
treated (for instance, the operating conditions acquired from the
wind turbines 2 may be averaged) regardless of reliability to
reduce the influence of the improper operating condition.
[0079] In this case, the operating condition acquisition part 16
may obtain a final operating condition by acquiring individual
operating conditions from the wind turbines 2 and averaging
parameters included in these operating conditions. In this case,
since an average of the parameters of the wind turbines 2 is used
as the operating condition, it is possible to acquire the operating
condition with a reduced influence of a random disturbance factor
that may be input to a specific wind turbine.
[0080] Further, even when the operating condition is acquired from
a single wind turbine 2, the operating condition acquisition part
16 may acquire the operating condition time-sequentially and apply
statistical processing such as averaging to the acquired data to
obtain an operating condition with higher reliability than an
operating condition that is instantaneously acquired.
[0081] Then, the estimated value calculation part 18 calculates an
estimated value of a physical quantity to be evaluated (step S2).
As the physical quantity, a physical quantity that can be measured
on the wind turbine facility 1 or the at least one wind turbine 2
(physical quantity that can be compared with an actual value
acquired by the actual value acquisition part 20) is selected. The
estimated value calculation part 18 acquires the operating
condition from the operating condition acquisition part 16 and
calculates an estimated value corresponding to the operating
condition. The calculation of the estimated value is performed
based on estimation logic that associates the operating condition
with the estimated value (i.e., estimation logic where the
operating condition is an input parameter, and the estimated value
is an output parameter).
[0082] The estimation logic may include at least one of a physical
model of the estimation target (wind turbine facility 1 or at least
one wind turbine 2), a machine learning model, or a statistical
processing logic, for instance. The estimation logic may be stored
in a storage device 26 such as a memory or a hard disc in advance,
and may be readable by the estimated value calculation part 18.
[0083] The physical model is a model constructed by simulating the
estimation target based on its physical characteristics such that
the estimated value is output as the output parameter when the
operating condition is input as the input parameter.
[0084] The operating condition input in the physical model may be
one operating condition or may be multiple operating conditions.
That is, in a case where the wind turbine facility 1 includes a
plurality of wind turbines 2, and the operating condition
acquisition part 16 acquires the operating condition from each of
the plurality of wind turbines 2, the estimated value may be
calculated by inputting each operating condition acquired from the
plurality of wind turbines 2 to the physical model.
[0085] Here, an example of the physical model used in the estimated
value calculation part 18 will be specifically described with
reference to FIG. 5. FIG. 5 is a schematic diagram of a physical
model 30 used in the estimated value calculation part 18. The
physical model 30 is a thermal equilibrium model related to a
cooling oil supply structure for the bearing 32 that rotatably
supports the rotor 2r of the wind turbine 2. The physical model 30
includes the bearing 32 rotatably supporting the rotor 2r of the
wind turbine 2, and a circulation channel 34 through which cooling
oil supplied to the bearing 32 circulates. On the circulation
channel 34, a cooler 36 comprising a heat exchanger for cooling the
cooling oil and a reservoir 38 for storing the cooling oil are
disposed.
[0086] In the physical model 30, the heat balance of the bearing 32
is represented by the following expression, using the amount of
heat J.sub.in [kJ] generated in the bearing 32 and the amount of
heat J.sub.out [kJ] cooled by the cooler 36:
J.sub.in-J.sub.out=H.sub.total.times.(T.sub.bt-T.sub.a) (1)
[0087] The parameters used in the expression (1) are as
follows:
H.sub.total: heat capacity [kJ/C.degree. ] of entire system
T.sub.bt: temperature [C.degree. ] of bearing 32 T.sub.a: ambient
temperature [C.degree. ]
[0088] The amount of heat J.sub.in [kJ] generated by rotation of
the wind turbine 2 and the amount of heat J.sub.out [kJ] cooled by
the cooler 36 are represented by the following expressions,
respectively:
J.sub.in=K1.times.M.times.n.times.t.sub.run (2-1);
J.sub.out=C.sub.cool.times.(T.sub.bt-T.sub.a).times.t.sub.cool
(2-2)
[0089] The parameter used in the expressions (2-1) and (2-2) are as
follows:
K1: proportionality constant of amount of generated heat to
friction in bearing 32 [-] M: friction moment of bearing 32 [Nmm]
n: rotational speed of bearing 32 [rpm] t.sub.run: running time of
wind turbine 2 [sec] C.sub.cool: cooling performance of cooler 36
[kW/C.degree. ] t.sub.cool: running time of cooler 36 [sec]
[0090] In the physical model 30 defined by the expressions (1),
(2-1), and (2-2), by inputting the input parameters (heat capacity
H.sub.total of entire system, ambient temperature T.sub.a,
proportionality constant K1, friction moment M, rotational speed n,
wind turbine running time t.sub.run, cooling performance C.sub.cool
of cooler, cooler running time tam') acquired as the operating
condition, the remaining parameter, i.e., the temperature T.sub.bt
of the bearing 32 is calculated as the estimated value.
[0091] The estimated value calculation part 18 may use a machine
learning model instead of the physical model. In this case, the
machine learning model can calculate an estimated value
corresponding to any operating condition by repeatedly learning a
relationship between the operating condition and the estimated
value using a predetermined algorithm. The machine learning model
applicable to the estimated value calculation part 18 may be any
known model and is not described in detail herein.
[0092] Alternatively, the estimated value calculation part 18 may
calculate the estimated value using a statistical processing logic
instead of the physical model and the machine learning model. In a
case where a plurality of actual values of the physical quantity
corresponding the estimated value can be acquired, the statistical
processing model calculates the estimated value by applying
statistical processing to the plurality of actual values. The
simplest statistical processing is, for instance, averaging or
calculation of the median or mode. For instance, in a case where
the wind turbine facility 1 includes a plurality of wind turbines
2, to calculate the estimated value of the wind turbine output
power under a certain operating condition, an actual value of the
output power of each wind turbine 2 is acquired, and an average of
the actual values is calculated as the estimated value.
[0093] Then, the actual value acquisition part 20 acquires an
actual value corresponding to the estimated value calculated by the
estimated value calculation part 18 (step S3). For instance, in a
case where the estimated value of the temperature of the bearing 32
is calculated using the physical model 30 shown in FIG. 5, a
temperature sensor (not shown) is provided to the bearing 32 to be
estimated, and the actual value acquisition part 20 acquires the
actual value by obtaining a detection signal from the temperature
sensor. The method of acquiring the actual value may be any method,
and any method according to the form of the actual value can be
used.
[0094] The determination part 22 determines whether an abnormality
is present in the estimation target by comparison between the
estimated value calculated by the estimated value calculation part
18 and the actual value acquired by the actual value acquisition
part 20 (step S4). Thus, by comparing the estimated value
calculated according to the operating condition with the actual
value, the determination part 22 can determine the presence of an
abnormality based on a criterion corresponding to the operating
condition. Therefore, compared with determination using a criterion
set uniformly regardless of the operating condition, a detailed
abnormality determination can be performed, and the operating state
can be accurately and early evaluated.
[0095] The determination part 22 may calculate a difference between
the estimated value and the actual value and determine whether an
abnormality is present based on whether the difference exceeds a
threshold. FIG. 6 is verification result showing estimated values
calculated based on the operating condition and actual values
regarding the temperature of the bearing 32, plotted against the
output power of the wind turbine 2 (output power of the generator
accommodated in the nacelle 10). In this example, the wind turbine
facility 1 includes four wind turbines 2 (unit No. 1 to unit No. 4)
of the same type disposed in a predetermined area, and the change
in temperature of the bearing 32 against the output power of the
wind turbine 2 is shown for each wind turbine 2.
[0096] The estimated value of the temperature of the bearing 32 is
a value calculated based on any of the physical model, the machine
learning model, or the statistical processing logic, as described
above. Accordingly, the actual value of a normal wind turbine 2
having no abnormality exhibits a similar tendency to the estimated
value. Thus, the determination part 22 determines whether an
abnormality is present by comparison between the actual value and
the estimated value of each wind turbine 2.
[0097] For the abnormality determination, the determination part 22
may calculate a difference (difference of absolute values) between
the estimated value and the actual value and determine whether an
abnormality is present based on whether the difference exceeds a
predetermined threshold. More specifically, the difference may be
calculated by subtraction between the absolute value of the
estimated value and the absolute value of the actual value (for
example, difference=|estimated value|-|actual value|). In this
case, in the example of FIG. 6, the differences of the units No. 1
to 3 are not more than the threshold while the difference of the
unit No. 4 is more than the threshold. Thus, it is quantitatively
determined that the unit No. 4 of the four wind turbines 2 has an
abnormality.
[0098] Further, in the abnormality determination, the determination
part 22 may identify a wind turbine having an abnormality by
comparison in behavior of the actual value among a plurality of
wind turbines. In a case where there is a plurality of wind
turbines 2 that are equivalent to each other, the behaviors of the
actual values of normal wind turbines 2 must be similar to each
other. Herein, the behavior indicates, for instance, comparison
with the actual values of the other wind turbines. On the other
hand, if there are wind turbines 2 that are equivalent to each
other but exhibit different behaviors in terms of the actual value,
it is highly possible that an abnormality is present in the wind
turbines 2. In the example of FIG. 6, since the unit No. 4 exhibits
a different behavior from the units No. 1 to 3, the unit No. 4 is
determined to have an abnormality. Thus, this method can identify a
wind turbine 2 having an abnormality by relatively comparing
behaviors of the actual values.
[0099] For the abnormality determination, the determination part 22
may obtain a correlation coefficient between the estimated value
and the actual value for each of the plurality of wind turbines 2
and determine that a wind turbine 2 whose correlation coefficient
exceeds a predetermined has an abnormality. The correlation
coefficient .gamma. is calculated from the following expression,
using the covariance .sigma..sub.xy.sup.2 of the estimated value
and the actual value, the standard deviation .sigma..sub.x of the
estimated value, and the standard deviation .sigma..sub.y of the
actual value:
.gamma.=.sigma..sub.xy.sup.2/.sigma..sub.x.sigma..sub.y (3)
[0100] The correlation coefficient is a value between -1 and +1. If
an abnormality is present, the correlation coefficient approaches 0
since the estimated value deviates from the actual value. The
threshold of the correlation coefficient may be obtained by
calculating an average value of the correlation coefficient and the
standard deviation of the plurality of wind turbines 2 and applying
the following expression: average value.+-.constant.times.standard
deviation (constant may be 3), for instance. FIG. 7 is verification
result in which estimated values calculated by the estimated value
calculation part 18 and actual values acquired by the actual value
acquisition part 20 regarding the temperature of the bearing 32 are
plotted for each operating condition. In a normal wind turbine 2
having no abnormality, since the estimated value coincides with the
actual value, the correlation coefficient is high, and the data
approximates the reference line R shown by the dotted line in FIG.
7. In contrast, in an abnormal wind turbine 2, the behavior
fluctuates due to failure of the machine, and the estimated value
is deviated from the actual value. As a result, the correlation
coefficient is low, and the data is deviated from the reference
line R shown by the dotted line in FIG. 7. In the present
embodiment, by calculating the correlation coefficient and
quantitatively evaluating the difference in tendency depending on
the presence or absence of an abnormality, it is possible to
accurately determine the presence or absence of an abnormality.
[0101] The determination result of the determination part 22 may be
output to the outside by the output part 24 (step S5). The output
part 24 may output the determination result to an external device
by an electrical signal or may output the determination result in a
form that appeals to an operator's sense. In the latter case, the
output part 24 may be a display device. In this case, the display
device may display an indicator of abnormality to notify the
operator.
[0102] As described above, according to the present embodiment,
comparing the estimated value calculated according to the operating
condition with the actual value enables determination based on a
criterion corresponding to the operating condition. Therefore,
compared with determination using a criterion set uniformly
regardless of the operating condition, a detailed abnormality
determination can be performed, and the operating state can be
accurately and early evaluated. Further, in case of the wind
turbine facility 1 including a plurality of wind turbines 2 of the
same type, by relatively comparing behaviors of the wind turbines
2, a more sensitive and precise evaluation can be performed than
conventional evaluation using a criterion set uniformly regardless
of the operating condition.
[0103] Embodiments for detecting an abnormality in each of a
plurality of wind turbines 2 based on abnormality degree E of each
wind turbine will be described with reference to FIGS. 8 to 11.
FIG. 8 is a block diagram showing an interior configuration and a
surrounding configuration of the control unit 14 according to an
embodiment of the present invention, in which the control unit 14
includes a first verification part 6 and a second verification part
7. FIG. 9 is a flowchart showing the operating state evaluation
method according to another embodiment of the present invention.
FIG. 10 is a diagram for describing an example of determination by
a first verification step (S95) according to an embodiment of the
present invention. FIG. 11 is a diagram for describing an example
of determination by a second verification step (S97) according to
an embodiment of the present invention.
[0104] There are known techniques for detecting an abnormality
based on abnormality degree E that can be calculated from multiple
parameters (state quantities) such as sensor values of multiple
sensors, for instance, Mahalanobis Taguchi method (MT method), one
class support vector machine (OCSVM), k-neighbor method (kNN), and
auto encoder. For instance, in the MT method, a normal group is
defined as a unit space based on multivariate data stored as
operating history, a distance (Mahalanobis distance) from the unit
space to target data is measured, and the distance is compared with
a threshold (abnormality determination threshold C) to determine an
abnormality. With this method, it is possible to comprehensively
diagnose each wind turbine 2 only with a single index, namely, the
Mahalanobis distance. Further, compared with a technique which
performs diagnosis based on whether each state quantity is below a
control value, the MT method can detect an abnormality early before
damage to devices progresses. By detecting such a sign of an
abnormality, it is possible to prevent or minimize damage to
devices in advance.
[0105] By applying such a technique to detect an abnormality of
each wind turbine 2, it is possible to relatively easily monitor
the operating state of each wind turbine 2 even with a number of
parameters to be monitored. As previously described, the operating
condition acquired from each wind turbine 2 may include detection
values (sensor values) of multiple sensors (not shown).
Accordingly, an abnormality degree E such as Mahalanobis distance
may be calculated based on a group of sensor values (operating
condition) detected at substantially the same timing from multiple
sensors disposed on each wind turbine 2, and it may be determined
whether an abnormality is present in each wind turbine 2 based on
comparison between the abnormality degree E and the abnormality
determination threshold C.
[0106] At this time, if detection sensitivity is increased by, for
instance, setting the abnormality determination threshold C low in
order to detect an abnormality at an early stage before the wind
turbine 2 fails, although the occurrence of failure of the wind
turbine 2 can be more reliably prevented, false detection may
occur, such as false abnormality detection when the abnormality
degree E temporarily exceeds the abnormality determination
threshold C due to an external environmental factor. If the wind
turbine 2 in which an abnormality is detected is stopped for
inspection, the operating rate of the wind turbine 2 decreases as
the number of false detections increases.
[0107] In view of this, in some embodiments, the control unit 14
may have a configuration to prevent false detection when
abnormality detection for each of a plurality of wind turbines 2 is
performed based on the abnormality degree E. More specifically, as
shown in FIG. 8, the control unit 14 may further include an
abnormality degree calculation part 4, an abnormality determination
part 5, and a first verification part 6.
[0108] Each functional part will now be described.
[0109] The abnormality degree calculation part 4 calculates the
abnormality degree E of each of a plurality of wind turbines 2
based on the operating condition of each wind turbine 2. The
plurality of wind turbines 2 may be a part of all wind turbines 2
included in the wind turbine facility 1. Further, the plurality of
wind turbines 2 may be two or more wind turbines 2 that are under
or assumed to be under the same environmental conditions (e.g.,
wind conditions, temperature conditions), for instance, which are
disposed close to each other geographically. The operating
condition of each wind turbine 2 includes multiple parameters.
Further, each parameter value is measured at a predetermined
timing, for instance periodically, by a corresponding sensor (not
shown) and is transmitted to the control unit 14 as the operating
condition. In the embodiment shown in FIG. 8, the operating
condition acquisition part 16 acquires the operating condition of
each wind turbine 2 transmitted from the wind turbine facility
1.
[0110] The abnormality determination part 5 determines whether an
abnormality is present in each of the plurality of wind turbines 2,
based on the abnormality degree E of each of the wind turbines 2
calculated by the abnormality degree calculation part 4. More
specifically, for each of the plurality of wind turbines 2, the
abnormality degree E of the wind turbine 2 may be compared with the
predetermined abnormality determination threshold C, and if the
abnormality degree E is not less than the abnormality determination
threshold C (E.gtoreq.C), it may be determined that an abnormality
is present, and if the abnormality degree E is less than the
abnormality determination threshold C (E<C), it may be
determined that an abnormality is not present (normal).
[0111] If the abnormality determination part 5 determines that at
least one of the plurality of wind turbines 2 has an abnormality
(abnormality positive determination Ja), the first verification
part 6 verifies that abnormality positive determination Ja. If
there are two or more wind turbines 2 that are determined to have
an abnormality (abnormality positive determination Ja), the first
verification part 6 verifies the abnormality positive determination
Ja of each of the two or more wind turbines 2 individually as the
verification target. For the verification, as shown in FIG. 8, the
first verification part 6 includes an other-result acquisition part
61 and a first validity determination part 62. The first
verification part 6 sequentially or parallel verifies each
abnormality positive determination Ja as follows.
[0112] The other-result acquisition part 61 acquires a
determination result regarding one or more other of the plurality
of wind turbines 2 based on the abnormality degree E, in a
predetermined period T including a timing of acquiring the
operating condition used for calculating the abnormality degree E
based on which the abnormality positive determination Ja to be
verified is made. That is, the acquiring timing is between the
start and end of the predetermined period T. The acquiring timing
may be a timing of measuring (detecting) sensor values of the
sensors (not shown). For instance, the acquiring timing may be
given by storing each sensor value with a time stamp indicating the
measurement time or the time series of the sensor values.
[0113] The predetermined period T may be any period, preferably a
period that can be evaluated that the environmental condition at
the acquiring timing of the wind turbine 2 with the abnormality
positive determination Ja to be currently verified is the same as
the environmental condition of the other wind turbines 2. For
instance, the wind condition such as wind speed acting on the wind
turbine blades 2b of the wind turbine 2 may have a time lag between
upstream and downstream sides along the wind direction. Further,
when the temperature around the wind turbine 2 changes according to
the wind condition, a time lag may occur in each wind turbine 2. By
determining the predetermined period T in consideration of such a
time lag, it is possible to match the acquiring timing of the
abnormality degree E of the wind turbine 2 to be verified and the
abnormality degree E of the other wind turbines 2.
[0114] The first validity determination part 62 determines whether
the abnormality positive determination Ja being verified is valid
(first validity determination), based on the number of wind
turbines 2 that is determined to have an abnormality as a result of
determination based on the abnormality degree E regarding the one
or more other wind turbines 2 as acquired by the other-result
acquisition part 61 (hereinafter, the number of abnormal wind
turbines Na). The number of abnormal wind turbines Na is any number
equal to or more than two. The number of abnormal wind turbines Na
may be determined based on the probability that abnormalities occur
in a plurality of wind turbines 2 at the same time as obtained from
previous results. In other words, the validity of each abnormality
positive determination Ja made by the abnormality determination
part 5 is judged based on a determination result regarding the
other wind turbines 2 made by the abnormality determination part
5.
[0115] Specifically, the first validity determination part 62 may
determine that the abnormality positive determination Ja currently
being verified is invalid if the number of abnormal wind turbines
Na is less than a first verification threshold Va (Na<Va), and
determines that the abnormality positive determination Ja currently
being verified is valid if the number of abnormal wind turbines Na
is not less than the first verification threshold Va
(Na.gtoreq.Va). In the embodiment shown in FIG. 8, with N being the
number of the plurality of wind turbines 2, if all of the other
wind turbines (N-1) are determined to have an abnormality (Va=N-1),
the abnormality positive determination Ja being verified is
regarded as due to an environmental factor and is determined to be
invalid. It is very unlikely that all the wind turbine 2 have an
abnormality at the same time. Therefore, when the validity of the
abnormality positive determination Ja being verified is denied and
the corresponding wind turbine 2 is determined to be normal, it is
unlikely that the wind turbine 2 that is determined to be normal
have an abnormality.
[0116] More specifically, in Case 1 shown in FIG. 10, the
abnormality positive determination Ja of the Xth wind turbine 2 is
the verification target, and all of the other N-1 wind turbines 2
are determined to have an abnormality (i.e., abnormality positive
determination Ja is made) at a timing in the predetermined period
T. Accordingly, in this case, the first validity determination part
62 determines that the abnormality positive determination Ja of the
Xth wind turbine 2 is invalid (the Xth wind turbine 2 is determined
to be normal). Conversely, in Case 2 shown in FIG. 10, the
abnormality positive determination Ja of the Xth wind turbine 2 is
the verification target, and all of the other N-1 wind turbines 2
are determined not to have an abnormality (i.e., not abnormality
positive determination Ja but abnormality negative determination Jn
is made) at a timing in the predetermined period T. Accordingly, in
this case, the first validity determination part 62 determines that
the abnormality positive determination Ja of the Xth wind turbine 2
is valid (the Xth wind turbine 2 is determined to be abnormal).
[0117] However, the present invention is not limited to the present
embodiment. For instance, the first validity determination part 62
may determine that the abnormality positive determination Ja of the
verification target is invalid if the abnormality positive
determination Ja is made on a predetermined proportion, for
instance, half ({N-1}/2) or more of the other wind turbines 2, or
if the abnormality positive determination Ja is made on a
predetermined number or more (e.g., two or more) of the other wind
turbines 2.
[0118] Further, in the embodiment shown in FIG. 8, the control unit
14 further includes a first notification part 64 that notifies that
an abnormality is detected if the first validity determination part
62 determines that the abnormality positive determination Ja of the
verification target is valid. In other words, the first
notification part 64 notifies that an abnormality is detected only
if there is the abnormality positive determination Ja that is
determined to be valid, and does not issue notification if none of
the abnormality positive determinations Ja is determined to be
valid as all of the wind turbines 2 are normal. Thus, it is
possible to avoid the notification of false detection and the need
for response to this notification such as inspection.
[0119] The operating state evaluation method corresponding to the
process executed by the control unit 14 having the above
configuration will now be described. In some embodiments, as shown
in FIG. 9, the operating state evaluation method may include an
abnormality degree calculation step (S92) of calculating the
abnormality degree E of each of the plurality of wind turbines 2
based on the operating condition of each of the wind turbines 2, an
abnormality determination step (S93) of determining whether an
abnormality is present in each of the plurality of wind turbines 2
based on the abnormality degree E of each of the wind turbines 2
calculated in the abnormality degree calculation step, and a first
verification step (S95) of verifying, if at least one of the
plurality of wind turbines 2 is determined to have an abnormality
(abnormality positive determination Ja) in the abnormality
determination step (S93), verifying that abnormality positive
determination Ja.
[0120] The first verification step (S95) includes an other-result
acquisition step (S95a) of acquiring a determination result
regarding one or more other of the plurality of wind turbines 2
based on the abnormality degree E, in a predetermined period T
including a timing of acquiring the operating condition used for
calculating the abnormality degree E based on which the abnormality
positive determination Ja to be verified is made, and a first
validity determination step (S95b) of making the first validity
determination, based on the number of wind turbines 2 that are
determined to be abnormal based on the abnormality degree E among
the one or more other wind turbines 2.
[0121] The abnormality degree calculation step (S92), the
abnormality determination step (S93), the first verification step
(S95) are same as the process executed by the abnormality degree
calculation part 4, the abnormality determination part 5, and the
first verification part 6 (other-result acquisition part 62 and
first validity determination part 62) described above, so that the
details will not be described again.
[0122] In the embodiment shown in FIG. 9, the operating state
evaluation method further includes a first notification step (S96b)
of notifying that an abnormality is detected if the abnormality
positive determination Ja is determined to be valid in the first
validity determination step (S95b). The first notification step
(S96b) is the same as the process executed by the first
notification part 64, so that the details will not be described
again.
[0123] The operating state evaluation method according to the
present embodiment will be described with reference to the
flowchart of FIG. 9.
[0124] In step S91, the operating condition of each of the
plurality of wind turbines 2 is acquired at a predetermined timing,
for instance, periodically. Then, the following steps (S91 to S98)
are performed as appropriated each time step S91 is performed. In
step S92, the abnormality degree calculation step is performed to
calculate the abnormality degree E of each wind turbine 2. In step
S93, the abnormality determination step is performed. More
specifically, in the embodiment shown in FIG. 9, the abnormality
degree E of each wind turbine 2 is compared with the abnormality
determination threshold C, and if E.gtoreq.C, the wind turbine 2 is
determined to be abnormal (abnormality positive determination Ja),
and if E<C, the wind turbine 2 is determined not to be abnormal
(abnormality negative determination Jn). In step S94, it is
determined whether there is the wind turbine 2 of E.gtoreq.C. In
step S94, if it is determined that there is the wind turbine 2 of
E.gtoreq.C, in step 95, the first verification step described above
is performed for each abnormality positive determination Ja made in
step S93.
[0125] More specifically, in the embodiment shown in FIG. 9, in
step S95a, the other-result acquisition step is performed to
acquire a determination result regarding the one or more other wind
turbines 2 based on the abnormality degree E. In step S95b, the
first validity determination step is performed, and if the number
of abnormal wind turbines Na is less than the first verification
threshold Va (Na<Va), the abnormality positive determination Ja
currently being verified is determined to be valid (the
corresponding wind turbine is determined to be abnormal). Then, in
step S95c, identification information of the wind turbine 2
corresponding to the valid abnormality positive determination Ja is
stored, followed by step S96. Conversely, in step S95b, if the
number of abnormal wind turbines Na is not less than the first
verification threshold Va (Na.gtoreq.Va), the abnormality positive
determination Ja currently being verified is determined to be
invalid (the corresponding wind turbine is determined to be
normal), and the method proceeds to step S96 without performing
step S95c.
[0126] Then, in step S96, if there is a plurality of abnormality
positive determinations Ja made in step S93, the other abnormality
positive determination Ja that has not been verified is selected as
the next verification target, and steps S95a to S95c are repeated.
Conversely, in step S96, if verification of all abnormality
positive determinations Ja is complete, in step S96b, the first
notification step is performed to notify that an abnormality is
detected in the wind turbine 2 corresponding to the identification
information stored in the step S95c.
[0127] With the above configuration, if at least one of the
plurality of wind turbines 2 is determined to have an abnormality
based on the abnormality degree E calculated based on the operating
condition (multiple parameter values), the validity (accuracy) of
that abnormality positive determination Ja is verified based on the
number of abnormality positive determinations Ja in the
determination result regarding the other wind turbines 2 based on
the abnormality degree E at the same timing. By ignoring the
abnormality positive determination Ja that is determined to be
false on the verification, it is possible to early detect a sign of
an abnormality occurring in each wind turbine 2 with an increased
detection sensitivity while avoiding false detection based on the
abnormality degree E of each wind turbine 2. Accordingly, it is
possible to prevent a reduction in operating rate due to false
detection and an increase in cost.
[0128] On the other hand, as described above, if detection
sensitivity is decreased by, for instance, increasing the
abnormality determination threshold C to detect an abnormality of
each of the plurality of wind turbines 2 based on comparison
between the abnormality degree E and the abnormality determination
threshold C, although false detection can be reduced, it is
difficult to early detect an abnormality (sign of abnormality)
before each wind turbine 2 fails. For instance, even if the
abnormality degree E gradually increases due to an abnormality
occurring in the wind turbine 2, if the value of the abnormality
degree E is not more than the abnormality determination threshold
C, an abnormality cannot be detected, and a sign of abnormality
cannot be accurately obtained. Further, as abnormality detection is
delayed, a risk of failure of the wind turbine 2 increases, and the
operating rate of the wind turbine 2 may decrease due to the
failure.
[0129] In view of this, in some embodiments, as shown in FIG. 8,
the control unit 14 may further include, in addition to the
abnormality degree calculation part 4 and the abnormality
determination part 5, a second verification part 7 configured to,
if at least one of the wind turbines 2 is determined not to have an
abnormality (abnormality negative determination Jn), verify that
abnormality negative determination Jn.
[0130] At this time, as shown in FIG. 8, the second verification
part 7 may perform verification if the abnormality determination
part 5 determines that none of the plurality of wind turbines 2 has
an abnormality. If there are two or more wind turbines 2 that are
determined not to have an abnormality (abnormality negative
determination Jn) by the abnormality determination part 5, the
second verification part 7 may verify the abnormality negative
determination Jn of each of the two or more wind turbines 2
individually as the verification target. For the verification, as
shown in FIG. 8, the second verification part 7 includes a
statistic calculation part 71, a relationship calculation part 72,
and a second validity determination part 73.
[0131] The statistic calculation part 71 calculates a statistic S
of the abnormality degree E of each of the plurality of wind
turbines 2. In the embodiment shown in FIG. 8, the statistic S is
an average of a plurality of abnormality degrees E.
[0132] The relationship calculation part 72 calculates a
relationship Sr between the abnormality degree E of each of the
plurality of wind turbines 2 and the statistic S calculated by the
statistic calculation part 71. In the embodiment shown in FIG. 8,
the relationship Sr is a deviation between the abnormality degree E
of each of the plurality of wind turbines 2 and the average of the
plurality of abnormality degrees E.
[0133] The second validity determination part 73 determines whether
the abnormality negative determination Jn of each of the plurality
of wind turbines 2 is valid (second validity determination) based
on the relationship Sr calculated by the relationship calculation
part 72. More specifically, if the relationship Sr is not less than
a second verification threshold Vb (Sr.gtoreq.Vb), the wind turbine
2 may be determined to be abnormal, and if the relationship Sr is
less than the second verification threshold Vb (Sr<Vb), the wind
turbine 2 may be determined to be normal.
[0134] The second verification threshold Vb may be set for each of
the plurality of wind turbines 2, and may be set based on previous
operating conditions (normal operating data) of the plurality of
wind turbines 2 in a normal state. Further, the second verification
threshold Vb may be set relatively low at an early stage, and the
second verification threshold Vb may be changed after some normal
operating data are collected. More specifically, the second
verification threshold Vb may be obtained from an average and a
maximum of the abnormality degrees E of the plurality of wind
turbines 2 in a normal state, using a function defining a
relationship of the average of the abnormality degrees E in a
normal state, the maximum of the abnormality degrees E in a normal
state, and the second verification threshold Vb.
[0135] In the example shown in FIG. 11, with respect to the Xth
wind turbine 2, after time t1, the deviation (relationship Sr)
between the abnormality degree E of the Xth wind turbine 2 and the
average is not less than the second verification threshold Vb, and
a relationship of Sr.gtoreq.Vb is established. Accordingly, the
second validity determination part 73 determines that the
abnormality negative determination Jn of the Xth wind turbine 2 is
invalid (the Xth wind turbine 2 is determined to be abnormal) on
the verification after time t1.
[0136] In the embodiment shown in FIG. 8, the control unit 14
further includes a second notification part 74 that issues
notification if the second validity determination part 73
determines that the abnormality negative determination Jn is
invalid. In other words, the second notification part 74 notifies
that an abnormality is detected only if the abnormality negative
determination Jn made by the abnormality determination part 5 is
determined to be invalid by the second validity determination part
73, and does not issue notification if all of the abnormality
negative determinations Jn made by the abnormality determination
part 5 are determined to be valid as all of the wind turbines 2
corresponding to the verified abnormality negative determinations
Jn are normal. Thus, it is possible to more appropriately detect an
abnormality.
[0137] The operating state evaluation method corresponding to the
process executed by the control unit 14 having the above
configuration will now be described. In some embodiments, as shown
in FIG. 9, the operating state evaluation method may include the
abnormality degree calculation step (S92), the abnormality
determination step (S93), and a second verification step (S97) of,
if at least one of the wind turbines 2 is determined not to have an
abnormality (abnormality negative determination Jn), verifying that
abnormality negative determination Jn.
[0138] Further, the second verification step includes a statistic
calculation step (S97a) of calculating the statistic S of the
abnormality degree E of each of the plurality of wind turbines 2, a
relationship calculation step (S97b) of calculating the
relationship Sr between the statistic calculated in the statistic
calculation step (S97a) and each of the abnormality degrees E of
the plurality of wind turbines 2, and a second validity
determination step (S97c) of making the second validity
determination, based on the relationship Sr calculated in the
relationship calculation step (S97b).
[0139] The abnormality degree calculation step (S92), the
abnormality determination step (S93), and the second verification
step (S97) are same as the process executed by the abnormality
degree calculation part 4, the abnormality determination part 5,
and the second verification part 7 (statistic calculation part 71,
relationship calculation part 72, and second validity determination
part 73) described above, so that the details will not be described
again.
[0140] In the embodiment shown in FIG. 9, the operating state
evaluation method further includes a second notification step (S98)
of issuing notification if the abnormality negative determination
Jn is determined to be invalid in the second validity determination
step (S97c). The second notification step (S98) is the same as the
process executed by the second notification part 74, so that the
details will not be described again.
[0141] The operating state evaluation method according to the
present embodiment will be described with reference to the
flowchart of FIG. 9.
[0142] Steps S91 to S94 have already been described, so that
description thereof will be omitted. In step S94, if there is no
wind turbine 2 that is determined to have an abnormality
(abnormality positive determination Ja), the method proceeds to
step S97. In other words, in the embodiment shown in FIG. 9, if all
of the plurality of wind turbines 2 are determined not to have an
abnormality (abnormality negative determination Jn), the method
proceeds to step S97. In step S97, the second verification step is
performed.
[0143] More specifically, in step S97a, the statistic calculation
step is performed to calculate the statistic S of the abnormality
degree E. In the embodiment shown in FIG. 9, the statistic S is an
average of the abnormality degrees E of the plurality of wind
turbines 2. In step S97b, the relationship calculation step (S97b)
is performed to calculate the relationship Sr between the
abnormality degree E of each of the plurality of wind turbines 2
and the statistic S of the abnormality degree E. In the embodiment
shown in FIG. 9, the relationship Sr is a deviation between the
abnormality degree E of each of the plurality of wind turbines 2
and the average of the plurality of abnormality degrees E.
[0144] Then, in step S97c, it is determined whether the
relationship Sr (deviation in FIG. 9) of each abnormality degree E
is not less than the second verification threshold Vb. In step S97,
if Sr.gtoreq.Vb, the abnormality negative determination Jn made in
step S92 is determined to be invalid (the corresponding wind
turbine is determined to be abnormal). Then, in step S97d,
identification information of the wind turbine 2 corresponding to
the abnormality degree E of Sr.gtoreq.Vb is stored, followed by
step S98. Conversely, if Sr<Vb, the abnormality negative
determination Jn made in step S93 is determined to be valid, and
the method proceeds to step S98 without performing step S97d. Then,
in step S98, the second notification step is performed to notify
that an abnormality is detected in the wind turbine 2 corresponding
to the identification information stored in the step S97d.
[0145] With the above configuration, if at least one of the
plurality of wind turbines 2 is determined not to have an
abnormality based on the abnormality degree E calculated based on
the operating condition (multiple parameter values), the validity
(accuracy) of that abnormality negative determination Jn is
verified based on the statistic S calculated from the plurality of
abnormality degrees E at the same timing. Thus, even if the
abnormality degree E is not more than the abnormality determination
threshold C, it is possible to early detect an abnormality, and it
is possible to prevent a reduction in operating rate due to failure
of the wind turbine 2 and an increase in cost.
[0146] However, the present invention is not limited to the above
described embodiments. In some embodiments, the abnormality
determination part 5 may not be provided, and abnormality detection
(notification) of the wind turbine 2 on which the abnormality
positive determination Ja is made may be performed based on the
relationship between the abnormality degree E of each of the
plurality of wind turbines 2 and the statistic S of the abnormality
degree E, without determination based on comparison between the
abnormality degree E calculated by the abnormality degree
calculation part 4 and the abnormality determination threshold
C.
[0147] Moreover, although in the embodiment shown in FIG. 9, in
step S94, the method proceeds to the first verification step (S95)
if there is the abnormality positive determination Ja, while the
method proceeds to the second verification step (S97) if there is
no abnormality positive determination Ja, in some embodiments, if
there are both the abnormality positive determination Ja and the
abnormality negative determination Jn, both the second verification
step (S95) and the second verification step (S97) may be performed
sequentially or parallel.
INDUSTRIAL APPLICABILITY
[0148] At least one embodiment of the present invention can be
applied to a method and a device for evaluating an operating state
of a wind turbine facility.
* * * * *